Project Summary Drug metabolites can have distinct efficacy and toxicity profiles relative to the parent compound, and interpersonal variation in drug metabolism can determine adverse reactions. Notably, oral drugs that exhibit low bioavailability, are delivered in delayed release formulations, or are excreted through bile can encounter enormous densities of bacteria in the gastrointestinal tract. The gut microbiome carries a collective gene content 150-fold larger than the human genome, encodes a rich repository of enzymes with the potential capacity to metabolize drugs, and varies widely between individuals. The microbiome thus has the potential to impact serum drug and metabolite exposure. However, scalable in vitro systems for identification of active microbial species, enzymes, and candidate drug metabolites are not available. Our understanding of microbiome-mediated drug metabolism is largely limited to anecdotal examples, and it is unknown whether gut microbes encode enzymes that metabolize many drugs. Strategies for determining the quantitative contribution of gut microbial drug metabolism to serum drug and metabolite exposure are also not available. We developed high-throughput approaches that generate metabolomic time-series profiles of the metabolism of hundreds of drugs by hundreds of human gut microbial communities and species, uncover the microbiome- encoded enzymes responsible for these transformations, and identify candidate drug metabolites produced as a result. We also provide evidence that combining gnotobiotics with physiologically-based pharmacokinetic modeling can quantitatively disentangle host and microbial contributions to serum drug metabolite exposure in vivo, using a drug that is converted into a single metabolite by host and microbiome as a proof of concept. In Aim 1 of this proposal, we present a plan to establish the first repertoire of drug-metabolizing enzymes in the human gut microbiome. These results will reveal whether human gut microbes encode enzymes that metabolize many drugs and whether microbiome genes encoding drug-metabolizing enzymes serve as predictive biomarkers of the drug-metabolizing capacity of an individual's microbiome. In Aim 2, we describe a strategy to develop and test generalized host-microbiome pharmacokinetic models that capture host and microbial conversion of a drug into multiple metabolites and include enterohepatic circulation in the model topology. If successful, these aims will establish broadly applicable approaches to identify when and how the microbiome could contribute to the metabolism of drugs and other small molecules.